Capsule Networks (CapsNets) [1]}, [2]}, [3]} were recently introduced to overcome the shortcomings of Convolutional Neural Networks (CNNs).
CNNs loose the spatial relationships between its parts because of max pooling layers, which progressively drop spatial information [1]}. Furthermore, CNNs are also commonly known as “black-box” models: most of the techniques providing interpretation over the model are post-hoc: they produce localized maps that highlight important regions in the image for predicting objects [5]}. CapsNets attempt to preserve and leverage an image representation as a hierarchy of parts, carving-out a parse tree from the networks. This is possible thanks to the iterative routing mechanism [1]} which models the connections between capsules. This can be seen as a parallel attention mechanism, where each active capsule can choose a capsule in the layer above to be its parent in the tree [1]}. Therefore, CapsNets can produce explainable representations encoded in the architecture itself [1]} yet can be still successfully applied to a number of applicative tasks[9]}, [10]}, [11]}.
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